CCO - Cloud Cost Optimizer

A. Yehoshua, I. Kolchinsky, A. Schuster
{"title":"CCO - Cloud Cost Optimizer","authors":"A. Yehoshua, I. Kolchinsky, A. Schuster","doi":"10.1145/3579370.3594746","DOIUrl":null,"url":null,"abstract":"Cloud computing can be complex, but optimal management of it doesn't have to be. In this paper, we present the design and implementation of a scalable multi-Cloud Cost Optimizer (CCO) that calculates the optimal deployment scheme for a given workload on public or hybrid clouds. The goal of CCO is to reduce monetary costs while taking into account the specifications of the workload, including resource requirements and constraints. By using a combination of meta-heuristics, CCO addresses the combinatorial complexity of the problem and currently supports AWS and Azure. The CCO tool [1], can be accessed through a web UI or API and supports on-demand and spot instances. For broad discussion refer to [2].","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th ACM International Conference on Systems and Storage","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3579370.3594746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Cloud computing can be complex, but optimal management of it doesn't have to be. In this paper, we present the design and implementation of a scalable multi-Cloud Cost Optimizer (CCO) that calculates the optimal deployment scheme for a given workload on public or hybrid clouds. The goal of CCO is to reduce monetary costs while taking into account the specifications of the workload, including resource requirements and constraints. By using a combination of meta-heuristics, CCO addresses the combinatorial complexity of the problem and currently supports AWS and Azure. The CCO tool [1], can be accessed through a web UI or API and supports on-demand and spot instances. For broad discussion refer to [2].
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
CCO -云成本优化器
云计算可能很复杂,但对它的最佳管理却不必如此。在本文中,我们介绍了一个可扩展的多云成本优化器(CCO)的设计和实现,它可以计算公共云或混合云上给定工作负载的最佳部署方案。CCO的目标是减少货币成本,同时考虑到工作负载的规格,包括资源需求和限制。通过使用元启发式的组合,CCO解决了问题的组合复杂性,目前支持AWS和Azure。CCO工具[1],可以通过web UI或API访问,并支持按需和现场实例。广泛讨论请参见[2]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Next-Generation Security Entity Linkage: Harnessing the Power of Knowledge Graphs and Large Language F3: Serving Files Efficiently in Serverless Computing Speeding up reconstruction of declustered RAID with special mapping Analyzing large-scale genomic data with cloud data lakes Fuzzing LibraryOSes for Iago vulnerabilities
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1